The rise of machine learning in the last decade has sparked interest in domain-specific compilers and their IRs, specifically for numerical computations over tensors — multidimensional arrays.
These specialized compilers such as TensorFlow [1]}, PyTorch [2]}, or TVM [3]} often use a graph-based representation.
These systems have developed into complex optimizing compilers having taken on inspirations from related compilers such as Halide [4]} and Lift [5]}, [6]}.
Lift builds on a tradition of similar functional languages and compilers that existed before, such as SaC [7]}.
More have emerged recently, including Accelerate [8]} and Futhark [9]} as well as Dex [10]} by Google research.
